Gene flow and genetic diversity of turtle grass, Thalassia testudinum, banks ex könig, in the lower Florida Keys

Aquatic Botany 61 (1998) 147±164

Gene flow and genetic diversity of turtle grass, Thalassia testudinum, banks ex koÈnig, in the lower Florida Keys Mark A. Schlueter*, Sheldon I. Guttman Department of Zoology, Miami University, Oxford, OH 45056, USA Received 2 April 1997; accepted 10 January 1998

Abstract Turtle grass (Thalassia testudinum, Banks ex KoÈnig) is the dominant seagrass in the very productive and valuable coastal seagrass community. The present study investigated genetic diversity and gene flow in turtle grass collected at 18 sites in the lower Florida Keys. Fourteen allozyme loci were resolved, of which 5 (ADH-2, GPI, LAP, 6-PDGH, and PGM-2) were variable. The mean heterozygosity for the 18 turtle grass sites was 0.027. The majority of genetic diversity measured occurred within populations, with a small proportion of genetic diversity measured among populations (GST ˆ0.050). The number of migrants each generation (Nm), an estimate of gene flow, was calculated using Wright's F-statistics. The F-statistics yielded an Nm ˆ24.8 for sites within the same area, Nm ˆ3.9 for adjacent sites (within 4 km), and an Nm ˆ1.0 among all 18 collection sites. These results indicated strong gene flow for sites that were adjacent, but not at a distance >4 km. Three Mantel tests were performed using a genetic distance matrix and three different geographic matrices to test which model of gene flow (isolation by distance, stepping stone, or island model) was appropriate for turtle grass. A stepping stone model closely predicted (pˆ0.0015) turtle grass gene flow; physical adjacency (stepping stone model) had a greater effect than geographic distance. Turtle grass sampled on two coral reef sites had significantly different allele frequencies at the ADH-2 locus from turtle grass on sites in the mangroves. The majority of other studies dealing with seagrass species have found little-to-no genetic variation; however, the present study has documented genetic variation in turtle grass at five allozyme loci. This variation suggests that sexual reproduction may significantly contribute to T. testudinum's genetic structure and evolution. However, overall genetic diversity was relatively low across all sites, indicating a trend towards genetic uniformity of turtle grass in the lower Florida Keys. This genetic uniformity may have contributed to the large turtle grass die-offs in recent years. # 1998 Elsevier Science B.V. Keywords: Alismatidae; Allozymes; Electrophoresis; Hydrocharitaceae; Seagrass

* Corresponding author. Tel.: (513) 529-3100; fax: (513) 529-6900; e-mail: [email protected] 0304-3770/98/$19.00 # 1998 Elsevier Science B.V. All rights reserved PII S 0 3 0 4 - 3 7 7 0 ( 9 8 ) 0 0 0 6 3 - 1

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1. Introduction 1.1. General background The seagrass community is one of the most productive and valuable coastal wetland resources. The Caribbean seagrass meadows, dominated by turtle grass (Thalassia testudinum, Banks ex KoÈnig), provide three key functions: (1) high productivity, (2) shelter, and (3) habitat stabilization (Zieman and Zieman, 1989; Zieman et al., 1989). Seagrasses have high growth rates (T. testudinum's growth rate was reported as high as 10 mm/day; Zieman, 1975). This growth (photosynthetically fixed energy) may be used directly (grazing) or indirectly (detritus) by the community's many inhabitants. Seagrass beds provide shelter to many epiphytic algae and animals. The seagrass meadows function as nursery sites for many commercially important marine fishes (grunts, snappers), invertebrates (shrimp, crabs), and as feeding sites for migrating shorebirds and waterfowl (Zieman, 1982; Phillips, 1984; Thayler et al., 1984). Seagrasses stabilize sediments by retarding currents, and reduce erosion by their well-knit matrix of roots and rhizomes (Zieman and Zieman, 1989). Water trapping by seagrasses has been documented (Powell and Schaffner, 1991) to keep water (thin layer <20 cm) on the banks for up to eight hours during low tides, thereby providing a permanent habitat for epibenthic fish and invertebrates. The importance of seagrass beds was documented during the Zostera marina L. (eel grass) `wasting disease' in 1931±33 (Cottam, 1934; Tutin, 1942). After the loss of 95% of the eel grass in North America, many commercially important fish and invertebrates disappeared or declined, and the number of overwintering birds decreased sharply (Adair et al., 1994). Currently, scientists and environmentalists are concerned about the rapid and widespread turtle grass die-off occurring in the Florida Bay. Since the summer of 1987, more than 4,000 ha of turtle grass beds have died, and it is estimated that another 23,000 ha have been affected (Robblee et al., 1991). Recurring die-offs since 1991 have further decreased the amount of healthy productive seagrass meadows, leading scientists to question the effects of habitat/nursery loss on the many fish and invertebrates residing in the seagrass community, as well as migrating and aquatic birds (Carlson et al., 1994). 1.2. Evolution and reproduction in T. testudinum Seagrasses (marine angiosperms) include 58 species in 12 genera, all contained in the sub-class Alismatidae (Kuo and McComb, 1989). Seagrasses date back to the late Cretaceous; however, they show low species diversity (Larkum and Hartog, 1989). A large number of seagrass fossils (including T. testudinum) representing most modern genera have been documented in the Eocene, and this evidence establishes that seagrasses were widely distributed at that time (Larkum and Hartog, 1989). The lack of species diversity and the antiquity of the species have led some to propose that an extremely slow rate of evolution is occurring within this group (Les, 1988; Cox and Humphries, 1993; Les et al., 1997). Interestingly, 75% of seagrasses are dioecious (including turtle grass), compared to only 4% among angiosperms (Cox and Humphries, 1993). With a high proportion of

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dioecy and a low rate of evolution, the seagrasses pose an interesting problem. The high level of dioecy has generally been explained as an advantage (i.e. resulting in high levels of genetic diversity from outcrossing) (Pettitt et al., 1981). Les (1988) challenged this point, stating that dioecy does not guarantee high outcrossing rates in seagrasses where sexual reproduction may be sporadic or insignificant. Les (1988) believed that dicliny exist as a relictual condition in hydrophiles, rather than a means to promote outcrossing. He hypothesized that four consequences of hydrophilous pollination are: (1) inefficient pollen transfer, (2) reduced sexuality, (3) widespread clonal growth, and (4) diminished seed production (Les, 1988). Les (1988) suggested that these ``traits may reflect a major adaptive shift towards asexuality as a means of preserving adaptive gene complexes in stable aquatic environments.'' Furthermore, he stated that little evidence existed for the three necessary conditions for outcrossing (sexuality, xenogamy, and genetic variable populations) in most extant species (Les, 1988). Laushman (1993) disagreed with Les, suggesting that aquatic habitats are not `stable,' and are probably less stable than many terrestrial habitats. Furthermore, Laushman (1993) indicated that finding genetic variation within hydrophilous populations should not be simply dismissed by defining its particular habitat as unstable. Turtle grass, like most seagrasses, has high amounts of vegetative growth through its rhizomes. Long-lived individuals can form independent ramets (clones) that allow stable communities of turtle grass to form. Sexual reproduction appears to occur less frequently in turtle grass, compared with vegetative/clonal growth. Gallegos et al. (1992) found that 17% of T. testudinum shoots contained flowers at least once during their lifetime. They determined the mean flowering frequency for the Mexican Caribbean turtle grass population to be 5,600 flowers per plastochrone interval (PI-1), which corresponded to one flowering event every 13 years. Thus, many short shoots would never flower in the shoot's lifetime (Gallegos et al., 1992). Seed production was rare-to-variable in T. testudinum (Johnson and Williams, 1982; Durako and Moffler, 1985a), and may be linked with the population's sex ratio (Durako and Moffler, 1985b). A wide range of sex ratios has been reported for T. testudinum, including populations containing only female flowers (Grey and Moffler, 1978; Durako and Moffler, 1985a; Les, 1988). Durako and Moffler (1985b) observed the highest seed production during periods of male bias, which occurred only in one out of three years of observations. They attributed yearly differences in sex ratios to the annual variation in the density of males; female densities remained constant temporally. Whether this low level of sexual reproduction is important in maintaining genetic diversity and adaptability of the species remains to be determined. Several studies have been conducted on genetic variability in seagrass populations (Les, 1988). These studies have led to conflicting generalizations about genetic diversity and recruitment. Most authors accept that aquatic plants have a pattern of low genetic variation, high rates of fixation, and low levels of sexual recombination (McMillan, 1982; Les, 1988; Laushman, 1993). Twenty-nine species of seagrasses have been surveyed for genetic variation using allozymes (Les, 1991). Over 86% (25 of 29 species) of seagrasses had no detectable variation at the enzymes surveyed (Gagnon et al., 1980; McMillan and Williams, 1980; McMillan, 1981; McMillan and Phillips, 1981; McMillan et al., 1981; McMillan, 1982; Les, 1986). However, three seagrass species Halodule uninervis (McMillan et al., 1981), Posidonia australis (Waycott, 1995; Waycott and Sampson,

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1997), and Zostera marina (eel grass) (Gagnon et al., 1980; Fain et al., 1992; Laushman, 1993) have genetically diverse populations. Older studies with small sample sizes, small numbers of populations sampled, and low numbers of enzyme systems surveyed may have inflated the number of invariant seagrass species. One of these older studies found no genetic variation in Posidonia australis (McMillan et al., 1981), whereas new studies (Waycott, 1995; Waycott and Sampson, 1997) have shown that this species is genetically variable at several enzyme loci. Turtle grass is the dominant seagrass in the Caribbean and is therefore a very significant component of the biota. In the present study, the genetic diversity and population genetic structure of turtle grass were examined using 14 enzyme systems across 18 populations. Allele frequency data from the 14 enzyme systems were used to calculate genetic diversity statistics and determine the pattern of gene flow within turtle grass populations. Nei's Gst values were calculated to determine the partitioning of genetic diversity within and between populations. Differences in allele frequencies between habitat types and geographic distance between collection sites were also examined. 2. Methods 2.1. Sampling Samples of turtle grass (Thalassia testudinum) were collected from 18 sites in the lower Florida Keys (Fig. 1). Between 24±35 different turtle grass samples were taken from each site. A sample consisted of several blades of grass attached to a stem. Each sample was collected roughly 2 m from the previous sample. This procedure was followed in order to reduce the chance that samples were from the same individual or rhizome. Samples were stored submerged 0.3±0.6 m underwater (in an estuarine side channel) for three to five days. The samples were then transported to Oxford, Ohio in plastic bags with sea water. Upon arrival (24 h), the samples were stored in a 58C environmental chamber. The samples remained there for the next 2 weeks, while electrophoretic analysis was performed. 2.2. Electrophoretic analysis Approximately 0.5 g of 50% stem and 50% leaf tissues were taken from each specimen for electrophoretic analysis. The tissues were ground in a buffer (Soltis et al., 1983) at a ratio of 1:1 (v:v) and then centrifuged for 3 min at 8,160 g. The supernatant was absorbed onto filter paper wicks and loaded into 15% starch gels (hydrolyzed potato starch; Sigma, St. Louis, MO). Fourteen gene loci from 10 enzyme systems were examined (Table 1). Gels were stained by standard methods (Selander et al., 1971; Harris and Hopkinson, 1976). Isozyme loci were numbered based on their migration distance from the origin; the locus with the greatest migration was labeled `one,' the locus with the second largest migration was labeled `two' and so on. Alleles were labeled in a similar manner. The allele with the greatest migration distance was labeled `a,' the one with the next largest migration distance was labeled `b,' and so on.

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Fig. 1. Map of the 18 turtle grass (Thalassia testudinum) sites in the lower Florida Keys.

2.3. Data analysis Attributes of the electrophoretic data including allele and genotype frequencies, mean heterozygosity, conformity to Hardy±Weinberg equilibrium, genetic variability estimates,

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Table 1 Enzyme systems, number of loci and corresponding gel buffer used for these systems Enzyme

Abbreviation

EC number

No. loci

Buffer systema

Alcohol dehydrogenase Glucose-6-phosphate isomerase Glutamate dehydrogense Isocitrate dehydrogenase Leucine aminopeptidase Malate dehydrogenase 6-Phosphogluconate dehydrogenase Phosphoglucomutase Superoxidase dismutase Triosephosphate isomerase

ADH GPI GDH IDHP LAP MDH 6-PDGH PGM SOD TPI

1.1.1.1 5.3.1.9 1.1.1.47 1.1.1.14 3.4.11.1 1.1.1.37 1.1.1.44 5.4.2.2 1.15.1.1 5.3.1.1

2 1 1 1 1 1 1 2 3 1

System System System System System System System System System System

a

5 5 6 5 6 9 11 11 9 11

(Soltis et al., 1983)

Wright's F-statistics, genetic distances and similarities, and phenograms were calculated utilizing the computer program BIOSYS-1 (Swofford and Selander, 1981). The Levene (1949) correction for small sample size was used. Genetic diversity statistics (DST, GST) were calculated using the expected heterozygosity values calculated by BIOSYS-1. Gene flow was measured in two ways. First, hierarchical F-statistics were calculated at two levels to examine gene frequency divergence among sites (Wright, 1940). The first hierarchy was constructed such that differences among sites within one area and among all sites were measured. The second hierarchy was constructed so that differences among adjacent sites (within 4 km) and all sites were measured. The number of migrants was estimated from the F-statistics using the formula: FST ˆ 1=…4Nm ‡ 1†; where Nm is the effective number of migrants per generation. The second method used to measure gene flow was the Slatkin (1985) rare allele model. Slatkin's model relies on the assumption that the frequency of rare alleles in subpopulations is a measure of the number of migrants between subpopulations. The model is: ln ‰p…1†Š ˆ aln…Nm † ‡ b; where p(1) is the average frequency of rare alleles found only in single populations, and `a' and `b' are constants. Dispersal was examined using three models of gene flow: (1) isolation by distance (where migrants disperse more frequently to locations in close proximity, and less frequently to distant locations, resulting in a positive correlation between geographical distance and genetic distance), (2) a stepping stone model (where migration occurs only between adjacent locations, resulting in a greater genetic similarity between populations which are adjacent), and (3) an island model (where migration occurs at random among locations; no correlation of genetic relatedness to either geographical distance or physical adjacency. Patterns of geographic variation were investigated using the Mantel test (Sokal, 1979) as implemented in the R-package (Legendre and Vaudor, 1991). The probabilities were

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computed in two different ways: by permutations (Hope, 1968) and by transforming the Z-statistic into another statistic, called t by Mantel (1967). The t-statistic is asymptotically distributed like a standard normal deviate. A matrix of the Nei (1972) genetic identity measures (GEN) for all pairwise comparisons of locations was tested against matrices measuring geographical characteristics of the locations. Three matrices of geographical relationships were constructed. The first (GEO) used the absolute distance (m) between all location pairs to test the isolation by distance model. The remaining two matrices used to test the stepping stone model were constructed with different stringences for the assignment of neighboring sites. The first adjacency matrix (ADJ-1) used a binary matrix where site pairs were considered adjacent only if they occurred on the same area/location (within 0.1 km or on the same reef). These were given a value of one and locations on different reefs were assigned a zero. The second adjacency matrix (ADJ-2) was less stringent; site pairs within 4 km of each other were considered adjacent. Site pairs less than 4 km apart were assigned a value of one, while sites greater than 4 km apart received a zero value. The 4 km value was chosen because it separated the coral reef sites and the mangrove sites, while still allowing mangrove sites to overlap. 3. Results Fourteen enzyme loci (from 10 different enzyme systems) were resolved. Five of the enzyme systems (ADH, GPI, LAP, 6-PDGH, and PGM) were variable. Allele frequencies at the 18 collection sites are reported in Table 2. Each genotype combination (for the 5 variable loci) present at each site and the number of individuals with that genotype are reported in Table 3. There were 23 genotype combinations. On average each site had 3.94 genotype combinations (indicating a multi±clonal pattern of genetic structure). The majority of the sites (11 out of 18) had one dominant genotype combination (>70% of the site's individuals) with 2 or 3 rare genotype combinations. Five of the remaining seven sites had two main genotype combinations (>80% of the site's individuals) along with 2 or 3 rare genotype combinations. Only Snipes #1 and Snipes #2 had several genotype combinations occurring at a moderate frequency. Each population's genotypic frequencies for each polymorphic locus were compared with those expected by Hardy±Weinberg predictions. There were 34 tests of which 11 (32%) were found to violate predictions. In each case, the deviation resulted from heterozygote deficiency. The mean expected heterozygosity was 0.027 and the mean number of alleles per locus (14 loci resolved) was 1.2. Percentage of polymorphic loci, mean number of alleles, and direct and expected heterozygosity values are reported for each population in Table 4. GST (proportion of genetic diversity among populations) was measured at 0.139 for the five variable loci, and GST ˆ0.050 for all 14 loci examined. There was genetic subdivision among several sites. Wright's hierarchical F-statistics showed a significant amount of gene flow between sites in the same area and between sites that were adjacent (within 4 km) (Table 5). Slatkin's rare allele model produced a Nm ˆ5.2 within adjacent sites (within 4 km) and a Nmˆ4.0 between all sites (greater than

a

30 0.08 0.92 0.00

30 0.00 0.00 1.00

30 0.05 0.95

30 0.00 1.00

30 0.00 1.00 0.00

30 0.13 0.87 0.00

30 0.00 0.00 1.00

30 0.00 1.00

30 0.00 1.00

30 0.00 1.00 0.00

24 0.04 0.96 0.00

24 0.00 1.00

24 0.00 1.00

24 0.00 0.00 1.00

24 0.08 0.21 0.71

3

Fig. 1 provides a key to sites.

b PGM-2 (n) a b 6PDGH (n) a b c

ADH-2 (n) a b c GPI (n) a b c LAP (n)

(Collection Sites)a Locus 1 2

24 0.00 1.00 0.00

24 0.00 1.00

24 0.00 1.00

24 0.00 0.00 1.00

24 0.00 0.35 0.65

4

30 0.07 0.93 0.00

30 0.00 1.00

30 0.00 1.00

30 0.00 0.00 1.00

30 0.02 0.65 0.33

5

24 0.00 1.00 0.00

24 0.00 1.00

24 0.00 1.00

24 0.00 0.00 1.00

24 0.06 0.92 0.02

6

24 0.02 0.98 0.00

24 0.06 0.94

24 0.00 1.00

24 0.00 0.00 1.00

24 0.00 0.96 0.04

7

24 0.00 1.00 0.00

24 0.00 1.00

24 0.00 1.00

24 0.00 0.02 0.98

24 0.00 0.58 0.42

8

24 0.00 1.00 0.00

24 0.00 1.00

24 0.00 1.00

24 0.00 0.00 1.00

24 0.08 0.92 0.00

9

24 0.02 0.98 0.00

24 0.08 0.92

24 0.00 1.00

24 0.00 0.00 1.00

24 0.00 0.79 0.21

10

Table 2 Allele frequencies at 5 polymorphic loci of turtle grass from 18 sites in the lower Florida Keys

24 0.00 1.00 0.00

24 0.00 1.00

24 0.00 1.00

24 0.00 0.00 1.00

24 0.00 0.73 0.27

11

24 0.04 0.96 0.00

24 0.02 0.98

24 0.00 1.00

24 0.00 0.00 1.00

24 0.06 0.92 0.02

12

24 0.00 1.00 0.00

24 0.00 1.00

24 0.00 1.00

24 0.00 0.00 1.00

24 0.00 0.94 0.06

13

25 0.00 1.00 0.00

25 0.00 1.00

25 0.00 1.00

25 0.06 0.00 0.94

25 0.10 0.44 0.46

14

30 0.00 0.97 0.03

30 0.00 1.00

30 0.02 0.98

30 0.00 0.00 1.00

30 0.02 0.55 0.43

15

35 0.10 0.90 0.00

35 0.00 1.00

35 0.00 1.00

35 0.00 0.00 1.00

35 0.09 0.70 0.21

16

24 0.06 0.94 0.00

24 0.00 1.00

24 0.00 1.00

24 0.00 0.00 1.00

24 0.00 0.96 0.04

17

24 0.08 0.92 0.00

24 0.00 1.00

24 0.00 1.00

24 0.00 0.00 1.00

24 0.00 0.96 0.04

18

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3 ± ± 2 ± ± ± ± ± ± ± ± ± ± ± ± ± 2 ± ± ± ± 6 14 4

4 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 3 ± ± ± ± 11 10 3

5 ± ± ± ± ± 1 ± ± ± ± ± ± ± ± ± 3 8 ± ± ± ± 16 2 5

1 ± ± ± 2 ± ± ± 4 ± ± ± ± ± ± ± ± 26 ± ± ± ± ± ± 3

ADH-2 GPI LAP PGM-2 6PDGH aa bb bb ab ab aa bb bb bb aa aa bb bb bb ab aa bb bb bb bb ab bb ab bb bc ab bb bb bb ab ab bb bb bb ab ab bb bb bb bb ac bb bb bb ab ac bb bb bb bb bb ab bb bb bb bb bb aa bb bb bb bb ab bb bb bb bb bb ab ab bb bb bb ab bb bb bb bb bb ab bb bb bb bb bb bb bb bb bb bc bc ab bb bb bb bc bb bb ab ab bc bb bb ab bb bc bb bb bb bb cc bb bb bb bb Different genotypes per population

2 ± ± ± 2 ± ± ± 1 ± ± ± 1 1 ± ± ± 25 ± ± ± ± ± ± 5

Turtle Grass Collection sites

Genotypes 6 ± ± ± 1 ± ± ± 1 ± ± ± 1 ± ± ± ± 21 ± ± ± ± ± ± 4

7 ± ± ± ± ± ± ± ± ± ± ± ± ± ± 1 ± 21 ± ± 1 1 ± ± 4

8 ± ± ± ± ± ± ± ± ± ± 1 ± ± ± ± ± 3 ± ± ± ± 21 ± 3

9 ± ± ± 1 ± ± ± 2 ± ± ± ± ± ± ± ± 21 ± ± ± ± ± ± 3

Table 3 The number of plants with each genotype combination for the five variable loci at the 18 collection sites

10 ± ± ± ± ± ± ± ± ± ± ± ± ± 1 3 ± 10 ± ± ± ± 10 ± 4

11 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 11 ± ± ± ± 13 ± 2

12 1 ± ± ± ± ± 1 ± ± ± ± ± ± ± ± ± 21 ± ± ± ± 1 ± 4

13 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 21 ± ± ± ± 3 ± 2

14 ± ± ± ± ± ± ± 3 ± 2 ± ± ± ± ± ± 4 ± 3 ± ± 8 4 6

15 ± ± ± ± 1 ± ± ± ± ± ± ± ± ± ± ± 11 1 ± ± ± 8 9 5

16 ± 1 1 ± ± 1 ± ± 1 ± ± ± ± ± ± 1 17 ± ± ± ± 7 1 8

17 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 3 19 ± ± ± ± 2 ± 3

18 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 4 18 ± ± ± ± 2 ± 3

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Table 4 Population genetic parameters for 18 sites of turtle grass Site number and name

Mean sample size per locus

Mean number of alleles

Percentage of Mean heterozygosity loci polymorphic Direct Count Expected

1. Middle Sambos Reef #1

30 (0.0) 30 (0.0) 24 (0.0) 24 (0.0) 30 (0.0) 24 (0.0) 24 (0.0) 24 (0.0) 24 (0.0) 24 (0.0) 24 (0.0) 24 (0.0) 24 (0.0) 25 (0.0) 30 (0.0) 34.7 (0.2) 24 (0.0) 24 (0.0) 26.0 (0.7)

1.1 (0.1) 1.1 (0.1) 1.2 (0.2) 1.1 (0.1) 1.2 (0.2) 1.1 (0.1) 1.2 (0.1) 1.1 (0.1) 1.1 (0.1) 1.2 (0.1) 1.1 (0.1) 1.3 (0.2) 1.1 (0.1) 1.1 (0.1) 1.3 (0.2) 1.2 (0.2) 1.1 (0.1) 1.1 (0.1) 1.2 (0.0)

7.1

2. Middle Sambos Reef #2 3. Pelican Shoals #1 4. Pelican Shoals #2 5. Boca Chica ± South 6. Boca Chica ± East 7. Similar Sound 8. Saddlebunch Channel 9. Saddlebunch Sound 10. Shark Key 11. Crane Key 12. Waltz Key 13. The Narrows 14. Snipe Key #1 15. Snipe Key #2 16. Content Key 17. Indian Key #1 18. Indian Key #2 Average

14.3 14.3 7.1 14.3 7.1 21.4 14.3 7.1 21.4 7.1 21.4 7.1 7.1 21.4 14.3 14.3 14.3 13.1 (1.2)

0.010 (0.010) 0.005 (0.003) 0.024 (0.018) 0.033 (0.033) 0.050 (0.041) 0.006 (0.006) 0.018 (0.010) 0.062 (0.059) 0.006 (0.006) 0.045 (0.031) 0.039 (0.039) 0.015 (0.008) 0.009 (0.009) 0.046 (0.046) 0.029 (0.021) 0.038 (0.029) 0.015 (0.010) 0.018 (0.013) 0.026 (0.026)

0.017 (0.017) 0.018 (0.013) 0.039 (0.033) 0.033 (0.033) 0.043 (0.034) 0.011 (0.011) 0.017 (0.010) 0.038 (0.035) 0.011 (0.011) 0.038 (0.026) 0.029 (0.029) 0.020 (0.012) 0.009 (0.009) 0.043 (0.043) 0.044 (0.037) 0.046 (0.035) 0.014 (0.010) 0.017 (0.012) 0.027 (0.026)

Standard errors are reported in parentheses

4 km apart). Using Nei's (1972) genetic identity measurements for all 18 sites, a phenogram was constructed (Fig. 2). The genetic identity measurement showed that the sites were very similar. All the sites exhibited a 0.975 or greater similarity to one another, with the exception of the two Pelican Shoals' sites which had a genetic identity value of 0.935 with all other sites.

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Table 5 Wright's hierarchical F-statistics combined across the five variable loci at the 18 sites examined Subgroup

Total group

F-statistic

Nm (Wright)

Site Site Site

Area (same area sites) Adjacent Sites (within a 4 km radius) All Sites

0.010 0.061 0.207

24.750 3.848 0.958

Fig. 2. Phenogram of the 18 turtle grass (Thalassia testudinum) sites in the lower Florida Keys.

Gene exchange resembled the stepping stone model of dispersal. This indicated that individuals were more likely to disperse to nearby adjacent habitats or demes. The results of the Mantel tests showed a significant relationship between genetic distance and both measures of site adjacency ( p ˆ0.0003 for sites at the same location; pˆ0.0015 for sites within 4 km); however, no significant ( pˆ0.4505) relationship was found between geographic distance and genetic distance (Table 6).

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Table 6 Results of Mantel tests for geographic variation of allele frequencies Test

n

GEN-GEO GEN-ADJ1 GEN-ADJ2

18 18 18

Z 4665.5438 165.7000 37.0000

p-value (Z )

t

p-value (t)

0.3546 0.0039 0.0040

0.1244 ÿ3.4853 ÿ2.9740

0.4505 0.0003 0.0015

n: number of sites; Z: Mantel statistic (Mantel, 1967; Hope, 1968); p: probability. t: statistic which is asymptotically distributed like a standard normal deviate (Mantel, 1967).

4. Discussion 4.1. Population genetic structure and gene flow All 18 populations showed some variation at the ADH-2 locus. Several sites exhibited variation at three (out of 14 loci) different loci. However, with the exception of the ADH-2 locus, variation within and between the sites is low. The uncommon/ rare allele never exceeds 9% frequency and is often considerably less than that. The genetic variation between the sites was also low based on Nei's (1972) genetic identity (Fig. 2). The growth and genetic diversity of aquatic plant populations may be strongly influenced by clonal growth (Waycott, 1995). Populations may be made up of a single genetic individual (genet) indicating a monoclonal structure or may be composed of several different clones, a multiclonal structure (Les, 1991). Although turtle grass reproduces sexually, it is believed to be secondary to clonal/vegetative reproduction for population growth and recovery from habitat disturbance (Gallegos et al., 1992). The results of our study show that turtle grass populations are on average made up of at least 4 genets, indicating a multiclonal population structure (Table 3). Twenty-three different genotype combinations existed in the 14 loci (5 variable) that were examined. Because we examined only a small portion (14 loci) of each individual's genetic make-up, it can be assumed that the number of genetically different individuals in each population and across populations is much higher. Sexual reproduction is believed to play a significant role in the genetic structure of turtle grass populations, especially based on the ADH-2 locus frequencies. However, without outcrossing rates, the importance of sexual reproduction in this system is difficult to assess. The results of this study indicated that turtle grass (T. testudinum) has a high level of gene flow. A stepping-stone pattern of gene flow was determined from the data, indicating high dispersal to adjacent habitats. Gene flow (Nm ˆ1) was found to exist even at large distances (>120 km) between sites. 4.2. Differences between sites As seen in the phenogram (Fig. 2), the 18 collection sites were very similar (0.975 or greater identity value) to one another, with the exception the Pelican Shoal sites. Although the two Pelican Shoal sites were significantly different from the other sites, they were similar to one another. There are two plausible explanations for the genetic

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Table 7 Comparison of allozyme variation at the species level between seven hydrophilous species (three aquatic plants and four seagrasses) and summary data for 473 non-hydrophilous taxa Group/Species

Source

Number of populations

Number of loci

Percent polymorphic loci

Non-hydrophilous taxa (Nˆ473) (Standard error) Hydrophilous taxa Ceratophyllum demersum Ceratophyllum echinatum Vallisneria americana (seagrasses) Amphibolis antarcticaa Posidonia australis Thalassia testudinum Zostera marina

Hamrick and Godt, 1990

12.7

16.5

50.5

(1.3)

(0.4)

(1.4)

Les, 1991 Les, 1991 Laushman, 1993

9 3 12

17 17 16

88.2 52.9 50.0

Waycott et al., 1996 Waycott and Sampson, 1997 Present study Laushman, 1993

13 7 18 7

14 16 14 24

0.0 56.3 35.7 50.0

a This species is included to represent all 25 invariant seagrass species. Twenty-five of the 29 (86.2%) seagrasses surveyed have been invariant (see text).

differences between these sites. First, the Pelican Shoal sites are isolated on a reef approximately 8 km from shore (seagrass beds located in the mangroves). The open ocean (containing deep areas where turtle grass can not survive) may act as a barrier reducing gene flow to and from the reef. Second, different environmental factors (e.g. current, light) may be present at each site, which may `select' for certain genotypes (Calem and Pierce, 1992). 4.3. Comparison of genetic diversity in seagrasses and other plants As previously stated, most scientists believe that aquatic plants have low levels of genetic variation, high rates of fixation, and low levels of sexual recombination compared to their terrestrial counterparts (McMillan, 1982; Les, 1988; Laushman, 1993). The present study shows that turtle grass does have low levels of genetic variation (Hexpˆ0.027) and high fixation rates (mean polymorphic loci ˆ35.7%). Table 7 compares allozyme variation at the species level, in non-hydrophilous (473 species; Hamrick and Godt, 1990), and hydrophilous taxa (3 non-seagrass species and 4 seagrass species). At the species level, non-seagrass hydrophilous taxa have a similar percent polymorphic loci; however, upon examination at a population level (Table 8) these taxa show considerably less allozyme variation than non-hydrophilous taxa. The three genetically variable seagrasses (Posidonia australis, Thalassia testudinum, and Zostera marina) had an average percent polymorphic loci of 47.3 at the species level, which was similar to the value (50.5) of non-hydrophilous taxa (Table 7). Although Halodule uninervis has been shown to be genetically variable in at least two populations (McMillan, 1982), insufficient data were available to include it in the comparison. However, like other hydrophilous taxa, the seagrasses have much lower allozyme

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Table 8 Comparison of allozyme variation within and among populations between seven hydrophilous species (three aquatic plants and four seagrasses) and summary data for 468 non-hydrophilous taxa Group/Species

Source

Mean percent polymorphic loci

Non-hydrophilous taxa

Hamrick and Godt, 1990

(Nˆ468) 34.2 1.5 0.113 0.224 (1.2)

(standard error) Hydrophilous taxa Ceratophyllum demersum Ceratophyllum echinatum Vallisneria americana (seagrasses) Amphibolis antarcticaa Posidonia australis Thalassia testudinum Zostera marina

A

HEXP

GST

(0.0)

(0.005)

(0.012)

Les, 1991 Les, 1991 Laushman, 1993

20.0 7.0 38.5

1.2 1.1 1.4

0.064 0.036 0.085

0.580 0.845 0.457

Waycott et al., 1996 Waycott and Sampson, 1997 Present study Laushman, 1993

0.0 33.8 13.1 18.3

1.0 1.5 1.2 1.3

0.000 0.110 0.027 0.063

0.000 0.229 0.050 0.107

a This species is included to represent all 25 invariant seagrass species. Twenty-five of the 29 (86.2%) seagrasses surveyed have been invariant (see text). The mean number of alleles is A, H exp is the mean heterozygosity expected under Hardy±Weinberg equilibrium conditions, and GST is Nei's measurement of differentiation among populations

variation (lower percent polymorphic loci, fewer mean number of alleles per locus and a lower mean heterozygosity) at the population level compared with non-hydrophilous taxa (Table 8). Our results on T. testudinum support current ideology that aquatic plants have less genetic diversity than terrestrial plants. 4.4. Effects of low genetic diversity Many species of seagrasses have been shown to be genetically invariant (Waycott, 1995), while the seagrasses that are variable, exhibit low levels of genetic diversity often across large areas (Laushman, 1993; Waycott et al., 1996; the present study). Ruckelshaus (1996) determined a high level of gene flow in eel grass (Z. marina) from neighborhood estimates, and the present study has found a high level of gene flow in turtle grass. If seagrasses do have a high level of gene flow, this may result in a lack of genetic isolation between populations contributing to the low diversification rates (and evolution) seen in marine angiosperms compared to their terrestrial counterparts (Ruckelshaus, 1996). From a genetic standpoint, low genetic diversity is equivalent to low adaptability. Each species must achieve a balance between short-term fitness (e.g. adaptation to the present environment) and long-term flexibility (e.g. genetic diversity) (Mather, 1953; Ledig, 1986). Scientists agree ``that change, or evolution, depends on the presence of genetic diversity, and that the long-term value of genetic diversity is probably proportional to its amount, which sets the limits to evolutionary change'' (Ledig, 1986). Therefore, the high level of genetic uniformity may have increased the susceptibility of Z. marina during the devastating `wasting disease' in 1931±33 (Cottam, 1931; Tutin,

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1942) by not offering an opportunity to take advantage of ecological or genetic refugia from the destructive agent (Ruckelshaus, 1996). The current die-off of turtle grass may be compared to the eel grass `wasting disease.' Both species exhibit low genetic diversity which may suggest a low ability to adapt to new stresses. Genetic diversity and heterozygosity have been related to disease resistance, growth, reproductive output, and fitness in many studies of plants (Ledig, 1986) and animals (Allendorf and Leary, 1986). Individuals with a heterozygous form of transferrin (ironbinding protein) showed a higher resistance to a number of iron-dependent microorganisms, including several pathogens (Schade and Caroline, 1944; Frelinger, 1972). Resistance to malignant falciparum malaria has been responsible for the maintenance of many human blood protein polymorphisms, such as sickle-cell anemia (Wills, 1981). The allele s at the û-hemoglobin locus, responsible for sickle-cell anemia, is found in populations only where malaria is prevalent (Allison, 1955). Since the homozygous recessive is often lethal without treatment, Allison (1955) and Wills (1981) theorize that the s allele is present at high frequencies, because of the resistance (increased fitness) of the heterozygotes to malaria. The s allele is `selected' against and does not exist in populations in regions without malaria or without a recent history of malaria (Allison, 1955; Wills, 1981). This negative `selection' (loss in genetic diversity) occurs because the s allele provides benefits only in malaria infested areas and has high costs (death or reduced fitness in homozygous individuals) (Wills, 1981; Ledig, 1986). Species that become highly adapted to certain environments and persist over long periods of time (e.g. seagrasses) would most likely lose or `select' against alleles with high costs, reducing that species genetic diversity. Seagrasses have persisted and thrived over millions of years (based on their fossil record; Larkum and Hartog (1989)). During the past century, man has increased his demands (e.g. water usage) and stresses (e.g. pollution) on the environment. These changes may have a significant impact on slowly evolving species, such as the seagrasses, making them more vulnerable to disease and other stresses (Muehlstein, 1989). Although the exact cause of the turtle grass die-off is not known, many possible reasons have been suggested: sediment sulfide toxicity, increased levels of hypoxia, the marine slime mold (Labyrinthula sp.), increased salinity levels, or a combination of these factors (Carlson et al., 1994; Durako and Kuss, 1994; Robblee et al., 1993). However, scientists do agree that high salinity in the Florida Bay, the result of water management practices in South Florida, and a decade-long drought, have provided additional stress weakening seagrasses (Carlson et al., 1994; Durako and Kuss, 1994). The diversion of upland runoff, resulting in increased salinity, has transformed Florida Bay from an estuary to a persistently hypersaline, marine lagoon (Fourqurean and Zieman, 1991; Durako and Kuss, 1994). Additional research studies need to be conducted on these important seagrass environments to understand how low genetic diversity and high levels of gene flow affect seagrasses and their ability to adapt to new stresses. In addition, water management practices of diverting water from the Everglades (and Florida Bay), along with other management practices should be reviewed if this ecologically and commercially important habitat is to be managed properly.

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